STT4SG-350: A Speech Corpus for All Swiss German Dialect Regions

We present STT4SG-350 (Speech-to-Text for Swiss German), a corpus of Swiss German speech, annotated with Standard German text at the sentence level. The data is collected using a web app in which the speakers are shown Standard German sentences, which they translate to Swiss German and record. We make the corpus publicly available. It contains 343 hours of speech from all dialect regions and is the largest public speech corpus for Swiss German to date. Application areas include automatic speech recognition (ASR), text-to-speech, dialect identification, and speaker recognition. Dialect information, age group, and gender of the 316 speakers are provided. Genders are equally represented and the corpus includes speakers of all ages. Roughly the same amount of speech is provided per dialect region, which makes the corpus ideally suited for experiments with speech technology for different dialects. We provide training, validation, and test splits of the data. The test set consists of the same spoken sentences for each dialect region and allows a fair evaluation of the quality of speech technologies in different dialects. We train an ASR model on the training set and achieve an average BLEU score of 74.7 on the test set. The model beats the best published BLEU scores on 2 other Swiss German ASR test sets, demonstrating the quality of the corpus

STT4SG-350 : a speech corpus for all Swiss German dialect regions

We present STT4SG-350, a corpus of Swiss German speech, annotated with Standard German text at the sentence level. The data is collected using a web app in which the speakers are shown Standard German sentences, which they translate to Swiss German and record. We make the corpus publicly available. It contains 343 hours of speech from all dialect regions and is the largest public speech corpus for Swiss German to date. Application areas include automatic speech recognition (ASR), text-to-speech, dialect identification, and speaker recognition. Dialect information, age group, and gender of the 316 speakers are provided. Genders are equally represented and the corpus includes speakers of all ages. Roughly the same amount of speech is provided per dialect region, which makes the corpus ideally suited for experiments with speech technology for different dialects. We provide training, validation, and test splits of the data. The test set consists of the same spoken sentences for each dialect region and allows a fair evaluation of the quality of speech technologies in different dialects. We train an ASR model on the training set and achieve an average BLEU score of 74.7 on the test set. The model beats the best published BLEU scores on 2 other Swiss German ASR test sets, demonstrating the quality of the corpus

SDS-200 : a Swiss German speech to Standard German text corpus

We present SDS-200, a corpus of Swiss German dialectal speech with Standard German text translations, annotated with dialect, age, and gender information of the speakers. The dataset allows for training speech translation, dialect recognition, and speech synthesis systems, among others. The data was collected using a web recording tool that is open to the public. Each participant was given a text in Standard German and asked to translate it to their Swiss German dialect before recording it. To increase the corpus quality, recordings were validated by other participants. The data consists of 200 hours of speech by around 4000 different speakers and covers a large part of the Swiss German dialect landscape. We release SDS-200 alongside a baseline speech translation model, which achieves a word error rate (WER) of 30.3 and a BLEU score of 53.1 on the SDS-200 test set. Furthermore, we use SDS-200 to fine-tune a pre-trained XLS-R model, achieving 21.6 WER and 64.0 BLEU

Rat GTP cyclohydrolase I is a homodecameric protein complex containing high-affinity calcium-binding sites

Recombinant rat liver GTP cyclohydrolase I has been prepared by heterologous gene expression in Escherichia coli and characterized by biochemical and biophysical methods. Correlation averaged electron micrograph images of preferentially oriented enzyme particles revealed a fivefold rotational symmetry of the doughnut-shaped views with an average particle diameter of 10 nm. Analytical ultracentrifugation and quantitative scanning transmission electron microscopy yielded average molecular masses of 270 kDa and 275 kDa, respectively. Like the Escherichia coli homolog, these findings suggest that the active enzyme forms a homodecameric protein complex consisting of two fivefold symmetric pentameric rings associated face-to-face. Examination of the amino acid sequence combined with calcium-binding experiments and mutational analysis revealed a high-affinity, EF-hand-like calcium-binding loop motif in eukaryotic enzyme species, which is absent in bacteria. Intrinsic fluorescence measurements yielded an approximate dissociation constant of 10 nM for calcium and no significant binding of magnesium. Interestingly, a loss of calcium-binding capacity observed for two rationally designed mutations within the presumed calcium-binding loop of the rat GTP cyclohydrolase I yielded a 45% decrease in enzyme activity. This finding suggests that failure of calcium binding may be the consequence of a mutation recently identified in the causative GTP cyclohydrolase I gene of patients suffering from dopa responsive dystonia

SDS-200: A Swiss German Speech to Standard German Text Corpus

We present SDS-200, a corpus of Swiss German dialectal speech with Standard German text translations, annotated with dialect, age, and gender information of the speakers. The dataset allows for training speech translation, dialect recognition, and speech synthesis systems, among others. The data was collected using a web recording tool that is open to the public. Each participant was given a text in Standard German and asked to translate it to their Swiss German dialect before recording it. To increase the corpus quality, recordings were validated by other participants. The data consists of 200 hours of speech by around 4000 different speakers and covers a large part of the Swiss-German dialect landscape. We release SDS-200 alongside a baseline speech translation model, which achieves a word error rate (WER) of 30.3 and a BLEU score of 53.1 on the SDS-200 test set. Furthermore, we use SDS-200 to fine-tune a pre-trained XLS-R model, achieving 21.6 WER and 64.0 BLEU